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| Οργανική Διαφορική Λειτουργία Αντικειμένου (Ordinal DIF)× | Έλεγχος Αναλλοιότητας Μετρήσεων× | |
|---|---|---|
| Πεδίο | Ψυχομετρία | Ψυχομετρία |
| Οικογένεια | Latent structure | Latent structure |
| Έτος προέλευσης≠ | 1999-2001 | 2000 |
| Δημιουργός≠ | Zumbo (logistic extension) and Penfield (Mantel generalization) | Vandenberg & Lance |
| Τύπος≠ | Item bias detection for ordered-category items | Multi-group confirmatory factor analysis procedure |
| Θεμελιώδης πηγή≠ | Zumbo, B. D. (1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (ordinal) item scores. Ottawa: Directorate of Human Resources Research and Evaluation, Department of National Defense. link ↗ | Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature. Organizational Research Methods, 3(1), 4–70. DOI ↗ |
| Εναλλακτικές ονομασίες | ordinal DIF, polytomous DIF, DIF for ordered categories, ordinal logistic DIF | Factorial Invariance, Measurement Equivalence, Configural-Metric-Scalar Testing, Ölçüm Değişmezliği |
| Συναφείς≠ | 6 | 3 |
| Σύνοψη≠ | Ordinal differential item functioning analysis detects whether an ordered-category item (such as a Likert-scale question) functions differently across demographic or cultural groups after controlling for the latent trait being measured. It extends classical binary DIF methods to polytomous response formats common in psychological and educational scales. | Measurement invariance testing is a sequence of nested confirmatory factor analysis (CFA) models that examines whether a psychological scale measures the same latent construct in the same way across distinct groups or time points. Systematized and popularized by Vandenberg and Lance (2000), the procedure tests a hierarchy of constraints — from identical factor patterns to identical item intercepts — so that researchers can justify meaningful group comparisons on latent means. |
| ScholarGateΣύνολο δεδομένων ↗ |
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